A reliability enhancement strategy of critical nodes in power system communication network based on quantitative calculation method and criticality index

Xiaoyu Deng; Yabin Chen; Yu Sui; Hao Yu

doi:10.4108/ew.10416

Authors

Xiaoyu Deng Power Grid Planning Research Center of Guangdong Power Grid Co., Ltd
Yabin Chen Power Grid Planning Research Center of Guangdong Power Grid Co., Ltd
Yu Sui Power Grid Planning Research Center of Guangdong Power Grid Co., Ltd
Hao Yu Power Grid Planning Research Center of Guangdong Power Grid Co., Ltd

DOI:

https://doi.org/10.4108/ew.10416

Keywords:

Power System Communication, Network Reliability, Criticality Index, Monte Carlo Simulation, Genetic Algorithm, Smart Grid Resilience

Abstract

INTRODUCTION: Power system communication networks are essential for smart grid operations, enabling real-time monitoring and control. Disruptions at critical communication nodes can jeopardize grid stability and lead to cascading failures, highlighting the need for accurate reliability assessment of these vital components. However, traditional methods often overlook the complex, dynamic, and interdependent nature of modern communication infrastructures.

OBJECTIVES: This paper aims to develop a precise and scalable methodology for assessing and enhancing the reliability of critical nodes in smart grid communication networks.

METHODS: The proposed approach integrates probabilistic failure modeling, graph-theoretic analysis, and heuristic optimization. Key techniques include a newly designed Criticality Index (CI) accounting for failure probabilities, repair dynamics, and topological relevance; a Monte Carlo simulation framework to assess network behavior under stochastic disturbances; and a genetic algorithm (GA) for optimizing node reinforcement strategies.

RESULTS: Experiments conducted on the IEEE-118 bus system demonstrate that the GA-CI methodology improves the Network Robustness Index by 12.45%, consistently outperforming baseline methods with acceptable computational efficiency.

CONCLUSION: The proposed framework provides a robust and interpretable solution for reinforcing critical communication infrastructure in smart grids. It holds potential for broader application in the reliability assessment of other complex networked systems.

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References

[1] Ferreira PDF, Carvalho PMS, Ferreira LAFM, et al. Distributed energy resources integration challenges in low-voltage networks: Voltage control limitations and risk of cascading. IEEE Trans Sustain Energy. 2012;4(1):82-88.

[2] Lai CM, Teh J. Network topology optimisation based on dynamic thermal rating and battery storage systems for improved wind penetration and reliability. Appl Energy. 2022;305:117837.

[3] Judge MA, Khan A, Manzoor A, et al. Overview of smart grid implementation: Frameworks, impact, performance and challenges. J Energy Storage. 2022;49:104056.

[4] Garip S, Özdemir Ş, Altın N. Power system reliability assessment—A review on analysis and evaluation methods. J Energy Syst. 2022;6(3):401-419.

[5] Liu X, Chen B, Chen C, et al. Electric power grid resilience with interdependencies between power and communication networks–a review[J]. IET Smart Grid, 2020, 3(2): 182-193.

[6] Pasham SD. Using graph theory to improve communication protocols in AI-powered IoT networks. Metascience. 2024;2(2):17-48.

[7] Breneman JE, Sahay C, Lewis EE. Introduction to reliability engineering. Hoboken: John Wiley & Sons; 2022.

[8] Aikman D, Galesic M, Gigerenzer G, et al. Taking uncertainty seriously: Simplicity versus complexity in financial regulation. Ind Corp Change. 2021;30(2):317-345.

[9] Jaskólski K. Availability of automatic identification system (AIS) based on spectral analysis of mean time to repair (MTTR) determined from dynamic data age. Remote Sens. 2022;14(15):3692.

[10] Shaker F, Shahin A, Jahanyan S. Simulating the corrective actions affecting system availability: a system dynamics approach. J Model Manag. 2024;19(6):1827-1848.

[11] Schwarze AC, Jiang J, Wray J, et al. Structural robustness and vulnerability of networks. arXiv preprint arXiv:2409.07498. 2024.

[12] Leino K, Wang Z, Fredrikson M. Globally-robust neural networks[C]//Proceedings of the International Conference on Machine Learning. PMLR; 2021:6212-6222.

[13] Wan Z, Mahajan Y, Kang BW, et al. A survey on centrality metrics and their network resilience analysis. IEEE Access. 2021;9:104773-104819.

[14] Ballester R, Casacuberta C, Escalera S. Topological data analysis for neural network analysis: A comprehensive survey. arXiv preprint arXiv:2312.05840. 2023.

[15] Fu Y, Ying F, Huang L, et al. Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM. Renew Energy. 2023;203:455-472.

[16] Malekipirbazari M, Aksakalli V, Shafqat W, et al. Performance comparison of feature selection and extraction methods with random instance selection. Expert Syst Appl. 2021;179:115072.

[17] Zhang J, Luo Y. Degree centrality, betweenness centrality, and closeness centrality in social network[C]//2017 2nd international conference on modelling, simulation and applied mathematics (MSAM2017). Atlantis press, 2017: 300-303.

[18] Chen W, Ishibuchi H, Shang K. Fast greedy subset selection from large candidate solution sets in evolutionary multiobjective optimization. IEEE Trans Evol Comput. 2021;26(4):750-764.